Deep　convolutional　neural　networks　can　extract　more　accurate　structural　information　via　deep　architectures　to　obtain　good　performance　in　image　super-resolution.　However,　it　is　not　easy　to　find　effect　of　important　layers　in　a　single　network　architecture　to　decrease　performance　of　super-resolution.　In　this　paper,　we　design　a　tree-guided　CNN　for　image　super-resolution　(TSRNet).　It　uses　a　tree　architecture　to　guide　a　deep　network　to　enhance　effect　of　key　nodes　to　amplify　the　relation　of　hierarchical　information　for　improving　the　ability　of　recovering　images.　To　prevent　insufficiency　of　the　obtained　structural　information,　cosine　transform　techniques　in　the　TSRNet　are　used　to　extract　cross-domain　information　to　improve　the　performance　of　image　super-resolution.　Adaptive　Nesterov　momentum　optimizer　(Adan)　is　applied　to　optimize　parameters　to　boost　effectiveness　of　training　a　super-resolution　model.　Extended　experiments　can　verify　superiority　of　the　proposed　TSRNet　for　restoring　high-quality　images.　©　1975-2011　IEEE.